**1. Introduction**

Interactions between human and the environment have attracted increasing attention [1]. In general, cross-sectoral issues involve a variety of social and natural knowledge [2]. Gaps between natural and social sciences imply that understanding the mechanisms underlying human–environment systems from a systematic perspective is crucial to sustainable development [3]. To a certain extent, several studies have reflected the fact that the problems are multi-scale and complex while the solutions are diverse [4,5]. Effective domestic policymaking hinges on diverse stakeholders, which plays a critical role in accelerating the localization of the SDGs (Sustainable Development Goals, proposed by the United Nations in 2015) [6]. National development plans for the 2030 Agenda in many countries try to position human, social, environmental, economic, and institutional objectives at the same level [7–9]. However, a scientific challenge that obviously exists in sustainable issues is trade-offs and compensation. The achievement of one SDG is often at the cost of sacrificing or assisting another [10–13].

By 2020, China achieved the goal of eliminating poverty, which was accompanied by economic growth and rapid urbanization and contributed to SDG 1 [14,15]. Unfortunately, inconsistent and rough terrestrial land development patterns in production space and living space have placed pressure on ecological protection and resource security for ecological spaces [16–19]. To control the intensity of terrestrial land development and adjust the spatial structure, the concept of PLE (production–living–ecology) space was proposed in the report of the 18th National Congress of the Communist Party of China. The objective is to construct intensive and efficient production space, livable and appropriate living space, and protected and beautiful ecological space with beautiful mountains and clear water based on the principle of balancing economic, social, and ecological benefits. It aims

**Citation:** Wang, D.; Fu, J.; Jiang, D. Optimization of Production–Living– Ecological Space in National Key Poverty-Stricken City of Southwest China. *Land* **2022**, *11*, 411. https:// doi.org/10.3390/land11030411

Academic Editor: Carlos Parra-López

Received: 28 February 2022 Accepted: 9 March 2022 Published: 11 March 2022

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to delimit the boundaries of multi-function terrestrial land development and establish a system for future sustainable land development [20]. To a certain extent, PLE space can be considered a combination of SDG indicators from a land function point of view (Figure 1). It is highly relevant to the 17 SDGs that involve environmental integrity, social equity, and economic prosperity, which comprise the triple bottom line approach of human wellbeing [21,22].

**Figure 1.** The relationship of PLE space and SDGs.

International academic research has discussed food security [23], tourism [24], agriculture [25], and urban regeneration [26] under the background of urban sustainability but rarely focuses on PLE space. After all, the concept has been proposed by the Chinese government in the context of China. Most studies are conducted on a domestic scope. Huang et al. claimed that the assessment of regional spatial carrying capacity and suitability is an important guideline for PLE space optimization [20]. Wu et al. indicated that the carrying capacity determines the upper limit of the PLE space in quantity, while suitability evaluation is related to the spatial layout structure [27]. Some studies have provided qualitative and empirical optimization suggestions based on the results of the carrying capacity and suitability evaluation [28–30]. In general, the suggestions point out that rules and regulations should be further enhanced and technology standards need to be improved in the future. However, it is uncertain whether these suggestions are feasible and effective. It is necessary to develop quantified and visualized tools to simulate future scenarios, especially for policymakers who face the difficulty of applying social and ecological approaches to decision-making [31].

Land resources are the most constrained factor for PLE space optimization. One parcel of land may be used as production space or as living space. In addition, land policy has become an indispensable means of macroeconomic regulation under China's national policies because land resources have both natural and economic properties. Therefore, some studies approach PLE space optimization as a mathematical problem of multi-objective optimization, guiding maximum economic benefits, social benefits, and ecological benefits [32]. However, two major gaps remain in the literature. Firstly, multi-objective optimization algorithms still face some challenges in flexibility and convergence, especially in preference adaptation for various formulations [33,34]. Secondly, difficulty and complexity increase when referencing spatial data, although addressing quantitative issues has significant advantages.

The system dynamics model seeks opportunities and ways to optimize the structure of the system from a holistic perspective based on the feedback characteristics of the internal components [35–37]. It fills the first gap presented above. In contrast to multiobjective optimization equations, the system dynamics model simulates the real world by establishing relationships between social and economic factors. This method can introduce

more factors and equations and perform dynamic simulations. SD models have been used in resource management, such as future urbanization and water scarcity [16], energy consumption [38], and water resource management systems [39]. However, the ability of the SD model to be applied in spatial allocation is very weak.

As for the other gap, the FLUS (Future Land Use Simulation) model (https://geosimulation. cn/FLUS.html, accessed on 15 February 2022) is used to simulate human activities and natural influences on land-use change and future scenarios. The model introduces an artificial neural network algorithm (ANN)-based probability calculation of suitability for various land use types based on traditional meta-automata. It proposes an adaptive inertial competition mechanism based on roulette selection (a stochastic selection method) [40], which can effectively deal with the uncertainty and complexity of multiple land-use types when they are transformed under the joint influence of natural effects and human activities, meaning the FLUS model has high simulation accuracy and can obtain similar results to the real land-use distribution [41]. To date, the model has been successfully applied in many cases, such as the simulation of future urban sprawl boundaries [42,43] and the simulation of flooding risks in rapid urbanization [44]. Furthermore, the input of future demand for land in the FLUS model can be determined by SD models, which means that the two can be well coupled. Sustainable development issues require a systematic approach to integrate various socioeconomic and environmental components that interact across regional levels, space, and time [45]. Some studies have integrated the idea of system science into the optimization of resource allocation [46–48] but rarely have focused on PLE space optimization. This paper aims to build an optimization model based on the SD and FLUS models, in which PLE space can be planned quantitatively and spatially under future scenarios.

Trade-offs and conflicts of the PLE function are more obvious and intense in the poor areas of southwest China. Yunnan Province has the highest number of poverty-stricken counties and includes Zhaotong city, which is located in Wumeng Mountain and was one of the 14 concentrated contiguous poverty-stricken areas in China until 2020 (Figure 2). The GDP per capita is far below the national average level. People's living standards and social development are limited by natural geographical conditions and ecosystem protection [49,50]. It is necessary to balance development and conservation when making future policies [51]. This paper has two main tasks, one is to develop a model applicable to the problem, and the other is to use the model to provide solutions for the future development of Zhaotong city, and attempt to answer the following questions: (1) In this impoverished region, what is the main trend in PLE space changes over the past few years? (2) Which scenario can meet the planning target of 2030? (3) How can the development pattern be optimized?

**Figure 2.** Location and slope map of Zhaotong city.

## **2. Methods and Data**

This paper aims to build an optimization model based on system dynamics and FLUS in which land resources can be planned quantitatively and spatially in terms of PLE space. A system dynamics model is employed to simulate the structure of the PLE space system and predict quantitative demand in 2030. FLUS is applied to allocate the land parcels at the spatial scale. The workflow is shown in Figure 3. The work consists of 4 main parts:


**Figure 3.** Workflow for the optimization.

#### *2.1. Classification of PLE Space*

The classification of PLE space is the basis for the optimization of space layout. For PLE space optimization, it is necessary to clarify the priority. This study proposes a new classification system. According to the production–living–ecological function, the first level of classification was carried out, and then the second category was determined based on the land-use data from 2010, 2015, and 2018.

#### 2.1.1. Detailed Data Sources

Based on the dataset of the multiperiod land cover dataset of China (MLC), the classification system of the "PLE space" is established. Detailed data sources are shown in Table 1. The MLC dataset was obtained by manual visual interpretation using Landsat remote sensing image data from the US. The land-use types include the six primary types of cropland, forestland, grassland, water, residential land, and unused land, and 25 secondary types. All raster datasets were resampled to 30 m by software Arcgis 10.2. As shown in Table 1, some datasets that are shared online publicly are limited due to temporal resolution.


